{"id":{"repo_id":"southwales","oai_identifier":"oai:pure.atira.dk:studenttheses/5ff75a8c-5a55-4672-91ec-4ffacb7e9284"},"canonical_url":"https://search.dev.ndltd.org/etd/southwales/oai:pure.atira.dk:studenttheses/5ff75a8c-5a55-4672-91ec-4ffacb7e9284","repository":{"repo_id":"southwales","name":"University of South Wales","base_url":"https://pure.southwales.ac.uk/ws/oai"},"display":{"title":"An Exploration of the relationship between Patient Reported Experience Measures (PREMS) and overall experience score using SERVQUAL","abstract":"Patient experience is a fundamental pillar of healthcare quality, yet while clinical outcomes have advanced significantly, the human element of care often struggles to keep pace. This research addresses a critical gap in healthcare quality improvement: the disconnect between the vast amounts of collected patient feedback and the intelligence required to genuinely transform service delivery. The study suggests that collecting data is insufficient; healthcare organisations must understand the statistical drivers of satisfaction to bridge the gap between practitioner perception and patient reality. While Healthcare facilities may not always be able to create the expected outcome for the patient, the patient experience through the system is something that can be influenced and managed by those that deliver the care.
Adopting a Positivist perspective, this study applies the SERVQUAL multi-dimensional model (Responsiveness, Assurance, Tangibles, Empathy, and Reliability) to repurpose existing legacy data within Swansea Bay University Health Board. The research employs a quantitative logistic regression and odds ratio methodology to analyse a dataset of 1,348 fully completed patient questionnaires containing 21 specific experience variables and an overall satisfaction score. To triangulate these findings against professional expectations, a novel data collection exercise was conducted with 18 expert participants (comprising clinical and managerial staff). These experts mapped the 21 questions to SERVQUAL dimensions and ranked them by perceived importance through three distinct lenses: clinical, managerial, and patient.
The statistical results challenge traditional assumptions regarding service quality in healthcare. The analysis identifies that Empathy and Responsiveness are the most powerful predictors of a positive patient experience (Odds Ratios of 24.22 and 19.85 respectively), whereas Reliability—often a primary operational focus—was the least influential dimension. Crucially, the triangulation of data reveals a significant misalignment between the priorities of clinical staff and the actual drivers of patient satisfaction, whereas management perspectives were statistically aligned with the patient data. This highlights a critical barrier to improvement: clinicians may be underestimating the value of \"soft\" skills compared to functional reliability.
The study contributes a generalisable methodological framework for healthcare organisations globally. It demonstrates that expensive new data collection is not always necessary; rather, by applying rigorous statistical models like SERVQUAL to existing datasets, organisations can unlock hidden insights. This research provides a practical roadmap for leaders to align staff training with the actual, rather than assumed, drivers of patient experience, moving from passive data collection to organisational intelligence.","abstract_html":"Patient experience is a fundamental pillar of healthcare quality, yet while clinical outcomes have advanced significantly, the human element of care often struggles to keep pace. This research addresses a critical gap in healthcare quality improvement: the disconnect between the vast amounts of collected patient feedback and the intelligence required to genuinely transform service delivery. The study suggests that collecting data is insufficient; healthcare organisations must understand the statistical drivers of satisfaction to bridge the gap between practitioner perception and patient reality. While Healthcare facilities may not always be able to create the expected outcome for the patient, the patient experience through the system is something that can be influenced and managed by those that deliver the care.<br/><br/>Adopting a Positivist perspective, this study applies the SERVQUAL multi-dimensional model (Responsiveness, Assurance, Tangibles, Empathy, and Reliability) to repurpose existing legacy data within Swansea Bay University Health Board. The research employs a quantitative logistic regression and odds ratio methodology to analyse a dataset of 1,348 fully completed patient questionnaires containing 21 specific experience variables and an overall satisfaction score. To triangulate these findings against professional expectations, a novel data collection exercise was conducted with 18 expert participants (comprising clinical and managerial staff). These experts mapped the 21 questions to SERVQUAL dimensions and ranked them by perceived importance through three distinct lenses: clinical, managerial, and patient.<br/><br/>The statistical results challenge traditional assumptions regarding service quality in healthcare. The analysis identifies that Empathy and Responsiveness are the most powerful predictors of a positive patient experience (Odds Ratios of 24.22 and 19.85 respectively), whereas Reliability—often a primary operational focus—was the least influential dimension. Crucially, the triangulation of data reveals a significant misalignment between the priorities of clinical staff and the actual drivers of patient satisfaction, whereas management perspectives were statistically aligned with the patient data. This highlights a critical barrier to improvement: clinicians may be underestimating the value of "soft" skills compared to functional reliability.<br/><br/>The study contributes a generalisable methodological framework for healthcare organisations globally. It demonstrates that expensive new data collection is not always necessary; rather, by applying rigorous statistical models like SERVQUAL to existing datasets, organisations can unlock hidden insights. This research provides a practical roadmap for leaders to align staff training with the actual, rather than assumed, drivers of patient experience, moving from passive data collection to organisational intelligence.","abstract_has_math":false,"creators":["Morgan, Lee"],"institution":null,"degree_name":"Doctoral Thesis","degree_level":"Student thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Proedrou, Filippos","Dolan, Gina"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026","date_published":"2026","updated_at":"2026-07-24T04:40:03Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:pure.atira.dk:studenttheses/5ff75a8c-5a55-4672-91ec-4ffacb7e9284"],"render_values":[{"text":"oai:pure.atira.dk:studenttheses/5ff75a8c-5a55-4672-91ec-4ffacb7e9284","href":null,"code":true}]}]},"links":{"outbound_url":"https://pure.southwales.ac.uk/en/studentTheses/5ff75a8c-5a55-4672-91ec-4ffacb7e9284","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Proedrou, Filippos","Dolan, Gina"]},{"key":"dc:creator","label":"Author","values":["Morgan, Lee"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2026"]},{"key":"dc:date.issued","label":"Date","values":["2026"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://pure.southwales.ac.uk/en/studentTheses/5ff75a8c-5a55-4672-91ec-4ffacb7e9284"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Student thesis"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctoral Thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:pure.atira.dk:studenttheses/5ff75a8c-5a55-4672-91ec-4ffacb7e9284","https://pure.southwales.ac.uk/en/studentTheses/5ff75a8c-5a55-4672-91ec-4ffacb7e9284"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://pure.southwales.ac.uk/files/37814466/DBA_Final_Submission_Lee_Morgan.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Patient experience is a fundamental pillar of healthcare quality, yet while clinical outcomes have advanced significantly, the human element of care often struggles to keep pace. This research addresses a critical gap in healthcare quality improvement: the disconnect between the vast amounts of collected patient feedback and the intelligence required to genuinely transform service delivery. The study suggests that collecting data is insufficient; healthcare organisations must understand the statistical drivers of satisfaction to bridge the gap between practitioner perception and patient reality. While Healthcare facilities may not always be able to create the expected outcome for the patient, the patient experience through the system is something that can be influenced and managed by those that deliver the care.
Adopting a Positivist perspective, this study applies the SERVQUAL multi-dimensional model (Responsiveness, Assurance, Tangibles, Empathy, and Reliability) to repurpose existing legacy data within Swansea Bay University Health Board. The research employs a quantitative logistic regression and odds ratio methodology to analyse a dataset of 1,348 fully completed patient questionnaires containing 21 specific experience variables and an overall satisfaction score. To triangulate these findings against professional expectations, a novel data collection exercise was conducted with 18 expert participants (comprising clinical and managerial staff). These experts mapped the 21 questions to SERVQUAL dimensions and ranked them by perceived importance through three distinct lenses: clinical, managerial, and patient.
The statistical results challenge traditional assumptions regarding service quality in healthcare. The analysis identifies that Empathy and Responsiveness are the most powerful predictors of a positive patient experience (Odds Ratios of 24.22 and 19.85 respectively), whereas Reliability—often a primary operational focus—was the least influential dimension. Crucially, the triangulation of data reveals a significant misalignment between the priorities of clinical staff and the actual drivers of patient satisfaction, whereas management perspectives were statistically aligned with the patient data. This highlights a critical barrier to improvement: clinicians may be underestimating the value of \"soft\" skills compared to functional reliability.
The study contributes a generalisable methodological framework for healthcare organisations globally. It demonstrates that expensive new data collection is not always necessary; rather, by applying rigorous statistical models like SERVQUAL to existing datasets, organisations can unlock hidden insights. This research provides a practical roadmap for leaders to align staff training with the actual, rather than assumed, drivers of patient experience, moving from passive data collection to organisational intelligence."]},{"key":"dc:title","label":"Title","values":["An Exploration of the relationship between Patient Reported Experience Measures (PREMS) and overall experience score using SERVQUAL"]}]}],"canonical_facts":{"dc:contributor.advisor":["Proedrou, Filippos","Dolan, Gina"],"dc:creator":["Morgan, Lee"],"dc:date":["2026"],"dc:date.issued":["2026"],"dc:description.abstract":["Patient experience is a fundamental pillar of healthcare quality, yet while clinical outcomes have advanced significantly, the human element of care often struggles to keep pace. This research addresses a critical gap in healthcare quality improvement: the disconnect between the vast amounts of collected patient feedback and the intelligence required to genuinely transform service delivery. The study suggests that collecting data is insufficient; healthcare organisations must understand the statistical drivers of satisfaction to bridge the gap between practitioner perception and patient reality. While Healthcare facilities may not always be able to create the expected outcome for the patient, the patient experience through the system is something that can be influenced and managed by those that deliver the care.
Adopting a Positivist perspective, this study applies the SERVQUAL multi-dimensional model (Responsiveness, Assurance, Tangibles, Empathy, and Reliability) to repurpose existing legacy data within Swansea Bay University Health Board. The research employs a quantitative logistic regression and odds ratio methodology to analyse a dataset of 1,348 fully completed patient questionnaires containing 21 specific experience variables and an overall satisfaction score. To triangulate these findings against professional expectations, a novel data collection exercise was conducted with 18 expert participants (comprising clinical and managerial staff). These experts mapped the 21 questions to SERVQUAL dimensions and ranked them by perceived importance through three distinct lenses: clinical, managerial, and patient.
The statistical results challenge traditional assumptions regarding service quality in healthcare. The analysis identifies that Empathy and Responsiveness are the most powerful predictors of a positive patient experience (Odds Ratios of 24.22 and 19.85 respectively), whereas Reliability—often a primary operational focus—was the least influential dimension. Crucially, the triangulation of data reveals a significant misalignment between the priorities of clinical staff and the actual drivers of patient satisfaction, whereas management perspectives were statistically aligned with the patient data. This highlights a critical barrier to improvement: clinicians may be underestimating the value of \"soft\" skills compared to functional reliability.
The study contributes a generalisable methodological framework for healthcare organisations globally. It demonstrates that expensive new data collection is not always necessary; rather, by applying rigorous statistical models like SERVQUAL to existing datasets, organisations can unlock hidden insights. This research provides a practical roadmap for leaders to align staff training with the actual, rather than assumed, drivers of patient experience, moving from passive data collection to organisational intelligence."],"dc:identifier":["oai:pure.atira.dk:studenttheses/5ff75a8c-5a55-4672-91ec-4ffacb7e9284","https://pure.southwales.ac.uk/en/studentTheses/5ff75a8c-5a55-4672-91ec-4ffacb7e9284"],"dc:identifier.uri":["https://pure.southwales.ac.uk/files/37814466/DBA_Final_Submission_Lee_Morgan.pdf"],"dc:language":["eng"],"dc:relation.isreferencedby":["https://pure.southwales.ac.uk/en/studentTheses/5ff75a8c-5a55-4672-91ec-4ffacb7e9284"],"dc:title":["An Exploration of the relationship between Patient Reported Experience Measures (PREMS) and overall experience score using SERVQUAL"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Student thesis"],"dc:type.qualificationname":["Doctoral Thesis"]},"updated_at":"2026-07-24T04:40:03Z"}